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Previous issue date: 2018-04-12 / We used the Infodengue database of incidence and weather time-series, to train predictive models for the weekly number of cases of dengue in 790 cities of Brazil. To overcome a limitation in the length of time-series available to train the model, we proposed using the time series of epidemiologically similar cities as predictors for the incidence of each city. As Machine Learning-based forecasting models have been used in recent years with reasonable success, in this work we compare three machine learning models: Random Forest, lasso and Long-short term memory neural network in their forecasting performance for all cities monitored by the Infodengue Project.
| Identifer | oai:union.ndltd.org:IBICT/oai:bibliotecadigital.fgv.br:10438/24093 |
| Date | 12 April 2018 |
| Creators | Mussumeci, Elisa |
| Contributors | Escolas::EMAp, Targino, Rodrigo dos Santos, Bastos, Leonardo Soares, Coelho, Flávio Codeço |
| Source Sets | IBICT Brazilian ETDs |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/publishedVersion, info:eu-repo/semantics/masterThesis |
| Source | reponame:Repositório Institucional do FGV, instname:Fundação Getulio Vargas, instacron:FGV |
| Rights | info:eu-repo/semantics/openAccess |
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